Interplay between intestinal microbiome and multimodal MRI brain biosignatures for prediction of response to anti-TNF therapy in ulcerative colitis

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Ulcerative colitis (UC) is one of the major entities of inflammatory bowel diseases (IBD), causing lifelong morbidity. Apart from diarrhea and rectal bleeding, abdominal pain and ongoing fatigue may represent a great illness burden. Introduction of anti-TNF therapy has made a major impact on the clinical benefit of the patient. Nevertheless, it is only effective in a subgroup of patients and there is still a discrepancy between objective control of inflammation and subjective central perception of symptoms and patient related outcomes, respectively. Previous work from our group demonstrated that functional magnetic resonance imaging (fMRI) was able to visualize a rapid decreased pain perception in the brain of IBD patients responding to anti-TNF antibody therapy. This was visible in primary nociceptive areas and limbic areas involved in pain experience, emotions, and body sensation, well before marked alleviation of mucosal inflammation can be achieved. These results might explain the prospective improvement of other clinical symptoms and renders those patients to become therapy responders in the long run. It has been shown that the microbiome affects the gut-brain axis and might also induce changes in brain function and behavior. However, changes in the gut microbial composition have not been directly correlated with MRI changes in UC patients so far. We postulate that an intestinal microbiome dependent disease subtype is reflected by a specific multimodal MRI (mMRI) biosignature and that this can be predictive for anti-TNF therapeutic efficacy. Within our project, we aim to better understand the correlation between the brain, gut microbial composition and prevalent disease activity after initiating anti-TNF therapy in UC patients. Based on our proof-of-concept studies, using the ideal infrastructure at Erlangen and supported by machine learning approaches we will therefore characterize the intestinal microbiome composition in UC patients in regard to response to anti-TNF therapy and correlate mMRI brain data with individual clinical parameters to establish patient subgroup differentiation to predict response to anti-TNF therapy. Furthermore, we aim to define gut microbiome specific, MRI biosignatures to delineate individual disease subtypes and predict response to anti-TNF efficacy in UC patients. Overall, defining specific individual subtypes of UC patients based on characterization of their intestinal microbiome composition in conjunction with individual MRI brain bio-signature and relate it to therapeutic response to anti-TNF therapy would not only increase our pathophysiological disease understanding, but could also open new ways for individualized patient stratification and prediction of treatment response.